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Rueckert, D.* ; Schnabel, J.A.*

Registration and segmentation in medical imaging.

In: Registration and Recognition in Images and Videos. 2014. 137-156 (Studies in Computational Intelligence ; 532)
DOI
The analysis of medical images plays an increasingly important role in many clinical applications. Different imaging modalities often provide complementary anatomical information about the underlying tissues such as the X-ray attenuation coefficients from X-ray computed tomography (CT), and proton density or proton relaxation times from magnetic resonance (MR) imaging. The images allow clinicians to gather information about the size, shape and spatial relationship between anatomical structures and any pathology, if present. Other imaging modalities provide functional information such as the blood flow or glucose metabolism from positron emission tomography (PET) or single-photon emission tomography (SPECT), and permit clinicians to study the relationship between anatomy and physiology. Finally, histological images provide another important source of information which depicts structures at a microscopic level of resolution. © 2014 Springer-Verlag Berlin Heidelberg.
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Publikationstyp Artikel: Sammelbandbeitrag/Buchkapitel
Korrespondenzautor
ISSN (print) / ISBN 1860-949X
Bandtitel Registration and Recognition in Images and Videos
Quellenangaben Band: 532, Heft: , Seiten: 137-156 Artikelnummer: , Supplement: ,
Nichtpatentliteratur Publikationen
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)